A transportation network is any navigable system of roads, pedestrian walkways, paths, rivers, shipping lanes or other network that is utilized to transport humans or vehicles. A transportation network can also include combinations of routes for the above modes of transportation. These combinations of routes are referred to as multimodal transportation networks. A segment of a transportation network (referred to as a transportation network segment) is a portion of the transportation network that represents a path of travel for a vehicle or pedestrian without a method of entry or exit other than at its end points.
A transportation network can be modeled and stored as a digital representation in a digital map database. In so doing, the transportation network is usually represented by geometry and associated attribution. Conventionally, in the context of a transportation network, attribution limits how travel can flow on the network. For example, attribution may include: speed of travel, whether or not a turn at an intersection is allowed, etc.
Geometry can be stored in the digital map database as a series of polylines connected at nodes. Polylines are a series of sequential coordinates that usually represent the centerline of a transportation segment. Nodes are connectors between the polylines, and generally occur at intersections where there is a decision point with respect to travel from one transportation network segment to another. Nodes can also occur at intersections with other map features such as a political boundary, geographic feature (e.g., a river), etc.
Alternatively, geometry can be stored in the digital map database by fitting a polyline to a path taken by a group of vehicles (a population). In this alternative example, the paths through an intersection for various maneuvers are disparate and the nodes represent locations where the paths (the statistics of the populations) on a road are indistinguishable. In this case, nodes do not coincide with intersections. Rather, nodes are placed at non-decision points where all traffic must travel in the same direction and there is no option to turn off a given path. In this example, geometry describes a maneuver or maneuvers, which defines the path taken between two non-decision points, and the nodes represent locations where one can transition between maneuvers.
Typically, digital transportation networks are created by traversing all paths/elements of the transportation network with highly specialized location measuring and recording systems designed for this purpose. In an alternative method, transportation network information is gleaned from aerial images or compiled from existing localized digital transportation networks.
One conventional method for creating a digital transportation network utilizes probe traces for updating/refining the transportation network. Probe traces are a plurality of sequential location measurements from location sensors. Typically, the location sensors are housed in a plurality of vehicles or carried by a plurality of pedestrians. But, conventional methods for creating digital transportation networks from uncoordinated probe traces are limited because the digital transport network must be “seeded” manually with at least an initial approximation of individual transportation elements. The probe traces are then used only to refine and improve accuracy. Conventionally, digital transportation networks are not generated or built (e.g., created from scratch) based on uncoordinated probe traces. In addition, the conventional art does not address the junction of transportation network segments in an automated fashion.
Recently, increasing numbers of vehicles are being equipped with Personal Navigation Devices (PNDs) or other device equipped to determine location. Conventional PNDs are capable of collecting and storing location information over time and uploading the location information to a central processor (or server) for analysis. This location information can be used to generate a representation of a transportation network segment as described above.
Location measurements from a single PND or similar device, however, are typically not sufficiently accurate to generate a digital transportation network for certain applications such as an Advance Driver Assistance System (ADAS). For example, positional accuracy for a road network in an ADAS should be less than about 5 meters. But, typical location measurements from conventional PNDs are on the order of about +/−10 to 15 meters.